49 lines
2.5 KiB
Python
49 lines
2.5 KiB
Python
# *****************************************************************************
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of the NVIDIA CORPORATION nor the
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# *****************************************************************************
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import torch
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class WaveGlowLoss(torch.nn.Module):
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def __init__(self, sigma=1.0):
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super(WaveGlowLoss, self).__init__()
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self.sigma = sigma
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def forward(self, model_output, clean_audio):
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# clean_audio is unused;
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z, log_s_list, log_det_W_list = model_output
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for i, log_s in enumerate(log_s_list):
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if i == 0:
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log_s_total = torch.sum(log_s)
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log_det_W_total = log_det_W_list[i]
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else:
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log_s_total = log_s_total + torch.sum(log_s)
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log_det_W_total += log_det_W_list[i]
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loss = torch.sum(
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z * z) / (2 * self.sigma * self.sigma) - log_s_total - log_det_W_total # noqa: E501
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return loss / (z.size(0) * z.size(1) * z.size(2))
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